Feed-forward Neural Networks (FNNs)

FNNs are among the simplest types of neural networks. Data enters through the input layer, passes through one or more hidden layers, and reaches the output layer without forming loops or cycles. Each neuron processes incoming information using weights, biases, and activation functions. During training, these parameters are adjusted to improve the network’s predictions.

FNNs provide a foundational architecture for understanding and building neural networks. They can learn complex relationships between inputs and outputs and serve as building blocks for many deep learning systems. Their relatively straightforward structure also makes them useful for a wide range of predictive tasks.